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This article was first published on June 1, 2023.
What is the Pinecone vector database?
In simple terms, Pinecone is a cloud-based vector database for machine learning applications.
By representing data as vectors, Pinecone can quickly search for similar data points in a database.
This makes it ideal for a range of use cases, including semantic search, similarity search for images and audio, recommendation systems, record matching, anomaly detection, and more.
If you're thinking, 'You call that simple?' then perhaps you're not familiar with vector databases.
What are vector databases?
Vector databases are designed to handle the unique structure of vector embeddings, which are dense vectors of numbers that represent text.
They're used in machine learning to capture the meaning of words and map their semantic meaning.
These databases index vectors for easy search and retrieval by comparing values and finding those that are most similar to one another, making them ideal for natural language processing and AI-driven applications.
Imagine a vector database as a vast warehouse and the AI as the skilled warehouse manager. In this warehouse, every item (data) is stored in a box (vector), organized neatly on shelves in a multidimensional space… for applications like recommendation systems, anomaly detection and natural language processing.
-- Mark Hingle, co-founder of TriggerMesh
Pinecone use cases
- Natural language processing: sentiment analysis, text classification, question answering
- Computer vision: object detection, image classification, face recognition
- Recommendation systems: recommend movies, music, and products to users and consumers
The timing of Pinecone's launch in 2021 was certainly fortuitous.
With the rise of generative AI in the latter half of 2022 and the massive interest in vector databases that accompanied it, Pinecone is now an industry leader.
In the beginning, most Pinecone use cases were centered around semantic search. Today, they have a broad customer base, from hobbyists interested in vector databases and embeddings to ML engineers, data scientists, and systems and production engineers who want to build chatbots, large language models, and more.
It was obvious to me that the world of machine learning and databases were on a head-on collision path where machine learning was representing data as these new objects called vectors that no database was really able to handle.
-- Edo Liberty, founder and CEO of Pinecone
Why use Pinecone with large language models?
Perhaps the biggest use case for the Pinecone vector database is natural language processing (NLP).
You can use Pinecone to build NLP systems that can understand the meaning of words and suggest similar text based on semantic similarity.
That's why Pinecone is so useful for large language models.
You can use Pinecone to extend LLMs with long-term memory. You begin with a general-purpose model, like GPT-4, but add your own data in the vector database.
This process is essential when considering how to build your own LLM model, as it allows you to fine-tune and customize prompt responses by querying relevant documents from your database to update the context.
You can also integrate Pinecone with LangChain, which combines multiple LLMs together.
This is the main reason vector databases are all the rage these days. And while there are some excellent open-source alternatives, such as Weaviate, Milvus, and Chroma, which are also big players, Pinecone remains the leader in this field.
Pinecone key features
- Speed: search and retrieve vectors quickly for applications that require real-time data processing
- Scalability: handle large datasets and high query loads
- Flexibility: use with a wide range of programming languages
If you’re a developer working with generative AI (that's probably most of you now), learning how to use Pinecone and similar vector databases will certainly be worth your time.
And if you need a web scraping tool to collect data for your vector databases, you might want to consider Website Content Crawler while you're at it.
Learn how to feed your large language models with web data using your favorite LLM integrations like LangChain, LlamaIndex, or Pinecone, and Apify Actors, like Website Content Crawler.
